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Trading in the Australian Stockmarket Using Artificial Neural Networks Bond University DOCTORAL THESIS Trading in the Australian Stockmarket Using Artificial Neural Networks Vanstone, Bruce J Award date: 2005 Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. School of Information Technology Bond University Trading in the Australian Stockmarket using Artificial Neural Networks by Bruce James Vanstone Submitted to Bond University in fulfillment of the requirements for the degree Doctor of Philosophy November 2005 Abstract This thesis focuses on training and testing neural networks for use within stockmarket trading systems. It creates and follows a well defined methodology for developing and benchmarking trading systems which contain neural networks. Four neural networks and consequently four trading systems are presented within this thesis. The neural networks are trained using all fundamental or all technical variables, and are trained on different segments of the Australian stockmarket, namely all ordinary shares, and the S&P/ASX200 constituents. Three of the four trading systems containing neural networks significantly outperform the respective buy-and-hold returns for their segments of the market, demonstrating that neural networks are suitable for inclusion in stockmarket trading systems. The fourth trading system performs poorly, and a number of reasons are proposed to explain the poor performance. It is significant, however, that the trading system development methodology defined in this thesis clearly exposes the potential failure when testing in-sample, long before the neural network would be used in real trading. Overall, this thesis concludes that neural networks are suitable for use within trading systems, and that trading systems developed using neural networks can be used to provide economically significant profits. i Statement of original authorship This thesis represents my own work and contains no material which has been previously submitted for a degree or diploma at this University or any other institution, except where due acknowledgement is made. Signature Witness Date ii Additional Publications The following is a list of publications by the candidate on matters relating to this thesis. Conference Vanstone, B. and C. N. W. Tan (2003). A Survey of the Application of Soft Computing to Investment and Financial Trading. Proceedings of the 8th Australian & New Zealand Intelligent Information Systems Conference (ANZIIS 2003), Sydney. Vanstone, B., G. Finnie, et al. (2004). Applying Fundamental Analysis and Neural Networks in the Australian Stockmarket. Proceedings of the International Conference on Artificial Intelligence in Science and Technology (AISAT 2004), Hobart, Tasmania. Vanstone, B., G. Finnie, et al. (2004). Enhancing Security Selection in the Australian Stockmarket using Fundamental Analysis and Neural Networks. Proceedings of the 8th IASTED International Conference on Artificial Intelligence and Soft Computing (ASC 2004), Marbella, Spain. Vanstone, B., G. Finnie, et al. (2005). Evaluating the Application of Neural Networks and Fundamental Analysis in the Australian Stockmarket. Proceedings of the IASTED International Conference on Computational Intelligence (CI 2005), Calgary, AB, Canada, ACTA Press. Book Chapter Vanstone, B. and C. N. W. Tan (2005). Artificial Neural Networks in Financial Trading. Encyclopedia of Information Science and Technology. M. Khosrow-Pour, Idea Group. 5: 163-167. iii Acknowledgments This thesis was jointly supervised by Professor Gavin Finnie and Adjunct Professor Clarence Tan, both from Bond University. I would like to take this opportunity to formally thank them both for the guiding influence and constructive comments they have made throughout this research. Further, I would also like to thank Professor Gavin Finnie for his contributions to my development in areas apart from research, such as in teaching, and for providing an academic role model for me to aspire to. I would also like to thank Associate Professor Kuldeep Kumar for the many times he offered his statistical expertise, which I was most grateful to receive. My thanks also go to Bond University for accepting me as a doctoral candidate, and for the provision of excellent research support and facilities. Without the level of research support I received at this university, from both academic and non-academic staff alike, the journey undertaken in this thesis would have been considerably more difficult. The contribution of numerous other people is also gratefully acknowledged. These include: • Graham Johnson and Brian Boniwell, both from Bond University library, for being ever willing to help with locating all manner of research publications, • Jim McElroy and Travis Johnston, both from IT Support, for being ever willing to assist with all manner of support issues that arose during the lifetime of this thesis iv Dedication This thesis is dedicated to my family. To my wife, Sue, and my children Daisy and Serena, for their continued support and understanding over the years that this journey has taken. To my parents, Patricia and James, for instilling in me the values of dedication and hard- work, without which this thesis may never have been completed. v Table of Abbreviations A variety of specialized abbreviations are used within this thesis. The more obscure of these terms are listed below. ADX: Average Directional Index AMEX: American Stock and Options Exchange AORD: Australian All Ordinaries Index ASX: Australian Stock Exchange ATR: Average True Range DAX: German Stock Exchange Index DJIA: Dow Jones Industrial Average FTSE: London Stock Exchange Index IBEX: Spanish Stock Exchange Index KOSPI: Korean Stock Exchange Index MACD: Moving Average Convergence/Divergence MOM: Momentum Indicator NIKKEI: Tokyo Stock Exchange Index NYSE: New York Stock Exchange RSI: Relative Strength Index SESALL: Singapore All Equities Index STOCHK: Stochastic (Momentum) Indicator TOPIX: Tokyo Stock Exchange Price Index TUNINDEX: Tunisian Stock Exchange Index vi Table of contents Chapter 1 Introduction................................................................................................... 21 1.1 Background to the research............................................................................... 21 1.2 Research problem and hypotheses.................................................................... 24 1.3 Justification for the research ............................................................................. 25 1.4 Outline of the report.......................................................................................... 27 1.5 Definitions......................................................................................................... 28 1.6 Delimitations of scope ...................................................................................... 28 Chapter 2 Literature Review.......................................................................................... 29 2.1 Introduction....................................................................................................... 29 2.2 Value Investment .............................................................................................. 30 2.2.1 Introduction............................................................................................... 30 2.2.2 Historical Evolution and Credibility......................................................... 30 2.2.3 Applicability ............................................................................................. 42 2.3 Technical Analysis............................................................................................ 43 2.3.1 Introduction............................................................................................... 43 2.3.2 Historical Evolution and Credibility......................................................... 43 2.3.3 Applicability ............................................................................................. 59 2.4 Soft Computing................................................................................................. 59 2.4.1 Introduction............................................................................................... 59 2.4.2 Historical Evolution and Credibility......................................................... 60 2.4.2.1 Soft Computing Classifications ............................................................ 60 2.4.2.2 Research into Time Series Prediction................................................... 61 2.4.2.3 Research into Pattern Recognition and Classification.......................... 65 2.4.2.4 Research into Optimization................................................................... 70 2.4.2.5 Research into Ensemble Approaches.................................................... 71 2.4.3 Applicability ............................................................................................. 75 2.5 Research Contributions....................................................................................
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